Throughout these experiments, we rely on two baselines: First, we compare against baselines that do not include any invariance constraints, i.e., models with \(\lambda =0\) .
Second, we compare against a non-compositional adversarial approach where we separately train \(K\)  distinct encoders and \(K\)  distinct adversaries for each of the \(K\)  sensitive attributes in the data.
This non-compositional adversary is essentially an extension of [1]}'s approach to the graph embedding domain.
